According to a global survey among marketing professionals in January 2025, approximately ** percent reported using artificial intelligence (AI) extensively in their data-driven marketing efforts. Around ** percent said they integrated AI in select areas, whereas ** percent were exploring AI, but have not implemented the technology. Some ** percent reported not having plans to use AI.

In late 2024, when a major logistics firm in Ohio replaced its legacy routing software with a generative AI system, delivery efficiency improved by 32% within three weeks. That wasn’t just an isolated success; it was a glimpse into the future. By mid-2025, AI has evolved from a futuristic novelty...

In a 2025 survey, around ** percent of respondants claimed to use AI tools intentionally on a daily basis either for personal use, work or study purposes. Similarly, ** percent reported to never use AI tools

AI tools continue expanding across daily life and business. From personalized learning to automated workflows, they fuel efficiency and reshape how people work. In healthcare, AI assists doctors in diagnostics, and in banking, it automates routine transactions and customer support. Readers can dive deeper into how AI is moving from...

According to a survey conducted in 2025, high school students were most likely to use artificial intelligence (AI) on a weekly basis in the United States, at ** percent, while ** percent used AI daily. In comparison, middle schoolers were equally likely to say that they used AI either daily or weekly, at ** percent.

Introduction

Generative AI statistics: From its early days in research labs to now, a global sensation, generative AI has completely changed the technology landscape. We are past the initial hype and now deep into a phase of actual application and major economic impact.

This article explores more into the data behind the rise of generative AI, giving you a clear, driven look at its current state and what's coming next. I've gathered all the latest information to give you the most detailed and accurate picture available, showing you exactly how this technology is changing everything, so let’s get into it.

During a global 2025 survey among marketing agencies and brands, approximately ** percent of respondents stated that they were using artificial intelligence (AI) for influencer marketing on a limited basis. Another **** percent of marketing professionals shared that they were using AI extensively, compared to *** percent who were not using or planning to use AI.

Jasper AI powers thousands of content teams with fast, scalable writing through templates and AI workflows. It fuels marketing and creative workflows, especially for mid-size enterprises and agencies. Today, Jasper is more than a writing tool; it’s an AI copilot. Explore how its performance, reach, and industry role shape content...

In the spring of 2025, a quiet disruption was brewing in the AI space. While tech giants were busy refining existing models, a fresh contender, DeepSeek AI, was stealthily gaining traction. Founded with a clear mission to democratize access to large language models and multimodal AI, DeepSeek AI didn’t just...

When Google unveiled Gemini AI, the tech world paused. Not just because it was another artificial intelligence launch, but because it promised something more: a multi-modal future. It was December 2023 when Sundar Pichai described Gemini not merely as a chatbot or assistant but as an evolving platform built to...

According to a 2025 survey of marketing professionals in the United States and Canada, ** percent of respondents said their organization was using artificial intelligence (AI) for generating insights and recommendations, and personalizing content at scale, respectively. Another ** percent were using AI for optimizing campaign journeys in their marketing processes. In comparison, ** percent were not actively using AI in marketing.

The statistic shows the cumulative revenues from the ten leading artificial intelligence (AI) use cases worldwide, between 2016 and 2025. Over the ten years between 2016 and 2025, AI software for vehicular object detection, identification, and avoidance is expected to generate * billion U.S. dollars.

ChatGPT was the chatbot that kickstarted the generative AI revolution, which has been responsible for hundreds of billions of dollars in data centres, graphics chips and AI startups. Launched by...

Character AI has surged into the spotlight, quietly building a user base of 20–28 million monthly active users while redefining digital interaction. In entertainment and creative industries, writers are using Character AI to prototype dialogue and scenarios faster than ever. Meanwhile, mental health researchers study its role in social support...

According to a survey about artificial intelligence (AI) adoption among small and medium-sized businesses in Australia, ***** percent of respondents stated that they currently use a generative AI assistant in their businesses as of July 2025. At the same time, ** percent stated that while they were aware of this AI application, they did not intend on adopting it into their business.

It’s 7:15 a.m. in a smart home in suburban Texas. The lights gradually turn on. A smartwatch buzzes with health data while a coffee machine starts brewing, all without human intervention. This isn’t the future; it’s the present-day norm in millions of households across the United States. As we step...

According to Cognitive Market Research, the global Ai Training Data market size is USD 1865.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 23.50% from 2023 to 2030.

The demand for Ai Training Data is rising due to the rising demand for labelled data and diversification of AI applications.
Demand for Image/Video remains higher in the Ai Training Data market.
The Healthcare category held the highest Ai Training Data market revenue share in 2023.
North American Ai Training Data will continue to lead, whereas the Asia-Pacific Ai Training Data market will experience the most substantial growth until 2030.

Market Dynamics of AI Training Data Market

Key Drivers of AI Training Data Market

Rising Demand for Industry-Specific Datasets to Provide Viable Market Output

A key driver in the AI Training Data market is the escalating demand for industry-specific datasets. As businesses across sectors increasingly adopt AI applications, the need for highly specialized and domain-specific training data becomes critical. Industries such as healthcare, finance, and automotive require datasets that reflect the nuances and complexities unique to their domains. This demand fuels the growth of providers offering curated datasets tailored to specific industries, ensuring that AI models are trained with relevant and representative data, leading to enhanced performance and accuracy in diverse applications.

In July 2021, Amazon and Hugging Face, a provider of open-source natural language processing (NLP) technologies, have collaborated. The objective of this partnership was to accelerate the deployment of sophisticated NLP capabilities while making it easier for businesses to use cutting-edge machine-learning models. Following this partnership, Hugging Face will suggest Amazon Web Services as a cloud service provider for its clients.

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Advancements in Data Labelling Technologies to Propel Market Growth

The continuous advancements in data labelling technologies serve as another significant driver for the AI Training Data market. Efficient and accurate labelling is essential for training robust AI models. Innovations in automated and semi-automated labelling tools, leveraging techniques like computer vision and natural language processing, streamline the data annotation process. These technologies not only improve the speed and scalability of dataset preparation but also contribute to the overall quality and consistency of labelled data. The adoption of advanced labelling solutions addresses industry challenges related to data annotation, driving the market forward amidst the increasing demand for high-quality training data.

In June 2021, Scale AI and MIT Media Lab, a Massachusetts Institute of Technology research centre, began working together. To help doctors treat patients more effectively, this cooperation attempted to utilize ML in healthcare.

www.ncbi.nlm.nih.gov/pmc/articles/PMC7325854/

Restraint Factors Of AI Training Data Market

Data Privacy and Security Concerns to Restrict Market Growth

A significant restraint in the AI Training Data market is the growing concern over data privacy and security. As the demand for diverse and expansive datasets rises, so does the need for sensitive information. However, the collection and utilization of personal or proprietary data raise ethical and privacy issues. Companies and data providers face challenges in ensuring compliance with regulations and safeguarding against unauthorized access or misuse of sensitive information. Addressing these concerns becomes imperative to gain user trust and navigate the evolving landscape of data protection laws, which, in turn, poses a restraint on the smooth progression of the AI Training Data market.

How did COVID–19 impact the Ai Training Data market?

The COVID-19 pandemic has had a multifaceted impact on the AI Training Data market. While the demand for AI solutions has accelerated across industries, the availability and collection of training data faced challenges. The pandemic disrupted traditional data collection methods, leading to a slowdown in the generation of labeled datasets due to restrictions on physical operations. Simultaneously, the surge in remote work and the increased reliance on AI-driven technologies for various applications fueled the need for diverse and relevant training data. This duali...

AI in Healthcare - Quick Overview Statistics

Artificial Intelligence in healthcare refers to the use of advanced computer algorithms and machine learning techniques to analyze data in the healthcare sector to provide better healthcare services.

AI helps healthcare providers make more accurate and real-time diagnoses, personalize treatment plans, and improve patient safety by identifying health risks earlier.

Types of AI Applications in Healthcare Statistics

• Medical imaging analysis
• Natural language processing (NLP)
• Disease prediction and risk assessment
• Virtual Assistants and Chabot’s
• Drug discovery and development
• Robot-assisted surgery
• Patient engagement
• Diagnosis and treatment
• Machine learning

In a 2025 survey, A combined **** percent of all U.S. respondents were comfortable to one degree or another with the use of AI for grocery shopping suggestions. Only *** percent of respondents were very uncomfortable.

According to a survey conducted in Japan in March 2025, **** percent of those respondents who use generative artificial intelligence (AI) with a PC did so in order to create and improve texts. The share was lower among people who use generative AI with a smartphone or tablet.